With rapid advance of technology, nuclear medicine imaging is becoming an effective diagnosis tool as it can be used for tracing a small amount of a radioisotope when the radioisotope is being dosed into a patient and permits specific physiological processes to be scrutinized at region of interest. The common devices that are used for nuclear medicine imaging are positron emission tomography (PET) instruments and single photon emission computed tomography (SPECT) instruments, just to name a few.
Taking SPECT for example, SPECT imaging is performed by using an imaging scanner to acquire multiple 2-D images from multiple angles. Thereafter, a computer is used to apply a image reconstruction algorithm to the multiple images, yielding a 3-D dataset. This dataset may then be manipulated to show thin slices along any chosen axis of the body. Nevertheless, during the image reconstruction, image degradation is generally induced due to the limited spatial resolution of the instrument used for acquiring the images, and thus the image quantitative accuracy can be adversely affected.
Assuming the image being detected is represented by the function g(x, y), the image degradation model can be represented by the following function:g(x,y)=H[f(x,y)]+n(x,y);                wherein, f(x, y) represent an original image,                    H represents a degradation function; and                            n(x, y) represents noise.For simplicity, H is generally assumed to be a linear spatial invariant, and thereby, the foregoing image degradation model is represented as following:g(x,y)=h(x,y)*f(x,y)+n(x,y),                                                wherein, the symbol “*” represents a mathematical operation of convolution; and the convolution in spatial domain and the multiplication in frequency domain make up a Fourier pair.The Fourier transformation for the aforesaid image degradation model in frequency domain is represented as following:G(u,v)=(u,v)*F(u,v)+N(u,v).Thereafter, the foregoing function of frequency domain is transformed back into a function of spatial domain that can be calculated using less time and resource. In a nuclear medical imaging application, when the original image f(x, y) is a point object, the application of h(x, y) upon the point object is going to cause an extended blur blob in an image that the original image is degenerated and spread. Consequently, the degradation function h(x, y) is called a point-spread function. The purpose of an image restoration operation is to acquire a measurement to an original image, so that it is preferred to have more information to the degradation function h(x, y) and the noise function n(x, y).        
Generally, for improving the resolution of quantitative analysis, a technique of image restoration is adopted for image quality enhancement, whereas such image restoration technique is being referred as de-blurring. The algorithms used for enabling image restoration can be divided into two categories according to their difference in nature, which are the direct method and the iteration method. Among which, the direct methods attempt to solve the problem by a finite sequence of operations so as to deliver an exact solution of the original image for the image degradation model, while the iterative method is a mathematical procedure that generates a sequence of improving approximate solutions to the original image. The most common iteration algorithm used is the Lucy Richardson (LR) algorithm.
For instance, an LR method for restoring degraded nuclear medical image is described in U.S. Pat. No. 7,899,254. The Richardson Lucy algorithms an iterative procedure for restoring a latent image that has been blurred by a known point spread function that is based upon the maximum likelihood estimation, and consequently, the following equation is used:
            f              (                  k          +          1                )              ⁡          (              x        ,        y            )        =                    f                  (          k          )                    ⁡              (                  x          ,          y                )              ⁡          [                        h          ⁡                      (                                          -                x                            ,                              -                y                                      )                          *                              g            ⁡                          (                              x                ,                y                            )                                                          g                              (                k                )                                      ⁡                          (                              x                ,                y                            )                                          ]      wherein g(k)(x,y)=h(x,y)*f(k)(x,y)
The concept of the aforesaid algorithm originated by treating an image data as a random quantity that is a statistical possibility resulting from other random quantities, whereas the aforesaid algorithm is performed to achieve a maximum value of that statistical possibility. It is noted that to enabling the aforesaid algorithm, all the pixel values of image data must be positive.
The biggest problem to the conventional LR algorithm is that: the LR algorithm assume that the degradation function h(x, y) is a linear spatially invariant, however, although the degradation in a nuclear medical image appears to be distributed according to a Gaussian distribution, the degradation function h(x, y) should be a spatial variable. Please refer to FIG. 1, which is curve diagram showing a measurement resolution of degradation for a microPET R4 system according to the paper, entitled “Performance evaluation of the microPET R4 PET scanner for rodents”, by Knoess C., Eur J Nucl Med Mol Imaging, 30(5), pp. 737-47, 2003. As shown in FIG. 1, the farther from the center of the system the blur become, and it will be a very time-consuming task if one intends to restore the blurred image pixel by pixel using their respective corresponding degradation functions. Taking a 512×512 image for example, there will be 262144 degradation functions that the time required for restoring the image can be multiplied. For saving time, conventionally the 512×512 image is divided into several sub-images and only some of the sub-images that contain vital information are selected for restoration. Another problem for the conventional LR algorithm is that: for different positions or objects in an image, the optimum number of iteration may not be the same. For instance, the objects that are comparatively smaller in an image may suffer more serious partial volume effect and thus may require more iterations in the LR algorithm to restore. Therefore, it is difficult to restore all aspect in one image by a fixed iteration number.
Therefore, it is require a method for image quality improvement and an imaging system thereof for solving the aforesaid shortcomings of the conventional LR algorithm.